Jev seems pretty cool! I just got access and have only gotten to do minimal experiments, but I love this general area of research and it fills a very real need.

I agree with you. I think the OPs pushback is emblematic of a larger reaction I've seen that is, at the very least, misinformed.

There are a lot of approaches that use a self-attention backbone for classifier-style outputs. You have structured generation libraries like SGLang and Outlines, but those basically give you guided generation on an autoregressive model. You also have a bunch of models that are non-autoregressive that try something similar. Older NLP stuff applies here, and there's newer stuff using diffusion transformers for this purpose.

But I don't think the Jev author has ever said that he's the sole human, alone in a vast sea of misguided researchers, who is interested in schema-guided classification? I think he said he found a novel way to train a model for this task that has much higher general intelligence at much lower cost than other approaches. Which is an exciting result with lots of applications if it bears out.

I think some people are just reflexively skeptical of anything that gets a lot of hype. Maybe that's fair. Things that are wildly successful and high impact also tend to get a lot of hype though, so it seems like a poor filter.

hey, you might wanna try this? - https://github.com/deepanwadhwa/OpenDecision

it's very similar to jev's api and runs locally - if you like it, you can try jev for your actual usecases.

In a world of agents, doing a BERT run takes about 2 hours from having an empty folder. Just a thought you could consider. Once you've done the first you can do the rest of them before the end of the work day.